gr_ll_ls: Gradient of the log-likelihood

In zthuang0422/PURM-2021-Latent-Stratification: Maximum Likelihood Latent Stratification

Description

Computes the first order partial derivative of the log-likelihood for the latent stratification model, with respect to every variable in the vector par.

Usage

gr_ll_ls(par, data, trans = FALSE)

Arguments

par	vector c(piA, piB, muA1, muA0, muB1, sigma), c(piA, piB/(1-piA), muA1, muA0, muB1, sigma) if trans=TRUE.
data	data frame containing columns y (positive outcome with zeros) and z (treatment).
trans	boolean signifying if piB has been transformed.

Details

For the input data frame, column z is the dummy variable for treatment. If z = 1, then the observation has received treatment. If z = 0, then the observation has not received treatment.
Sometimes piB is transformed to relative proportions from absolute proportions. This transformation allows the reparameterization of the piA and piB to allow constraint bounds between 0 and 1 in the optimization procedure.
The output vector is named, each representing the gradient taken with respect to that variable in the parameter.

Value

Gradient of the log-likilihood for the latent stratification model as a named vector.

Examples

sim = sim_latent_strat(n=10000, piA=0.2, piB=0.1, muA1=5, muA0=4.5, muB1=3, sigma=0.3)
gr_ll_ls(sim$par, sim$data)

# if the strata proportions are in relative sizes
gr_ll_ls(sim$par, sim$data, trans=TRUE)

zthuang0422/PURM-2021-Latent-Stratification documentation built on Dec. 23, 2021, 10:12 p.m.